大型语言模型 (LLM) 在瘤学中的时间演变
Zilin Qiu1, Aimin Jiang2, Chang Qi3
1Department of Oncology, Zhujiang Hospital, Southern Medical University, Donghai County People's Hospital (Affiliated Kangda College of Nanjing Medical University), Lianyungang, 222000, China.
Journal of translational medicine
|November 4, 2025
概括
在瘤学中,大型语言模型 (LLM) 显示性能随着时间的推移而下降,Gemini改进,而ChatGPT模型退化. 这影响了基于证据的LLM在癌症护理中的部署.
科学领域:
- 在瘤学中使用人工智能
- 医学中的自然语言处理.
- 机器学习用于医疗保健
背景情况:
- 大型语言模型 (LLM) 在医疗保健中越来越多地使用,但它们在瘤学等专业领域的准确性可能会受到知识衰退和概念漂移的影响.
- 影响瘤学问答LLM成绩的时间动态需要进行系统的评估.
- 本研究评估了使用现实数据对瘤学相关查询的LLM准确性的演变.
研究的目的:
- 系统地评估不同LLM的时间性能趋势,以回答与瘤学相关的问题.
- 为了比较ChatGPT-3.5,ChatGPT-4和双子座在瘤学中的准确性演变.
- 为临床瘤学环境中基于证据的LLM部署提供见解.
主要方法:
- 通过2025年进行了系统的文献搜索,使用与LLM和癌症研究相关的关键词.
- 编制了614个瘤学研究问题的数据集 (主观和多选题).
- 通过使用标准化评分方法和统计分析 (随机/固定效应模型),随着时间的推移评估了ChatGPT-3.5,ChatGPT-4和Gemini的响应的准确性.
主要成果:
- 随着时间的推移,ChatGPT-3.5和ChatGPT-4在主观和客观瘤学问题的评估中表现下降.
- 在研究期间,双子座对两种类型的问题都显示出显著的绩效改善.
- 聊天GPT-3.5经历了显著的性能转变,随着时间的推移而退化,特别是在主观瘤学问题上.
结论:
- 分析表明,在瘤学领域,ChatGPT-3.5和ChatGPT-4的时间性能下降.
- 双子座随着时间的推移表现出一致的性能改善,与其他模型中观察到的退化形成鲜明对比.
- 调查结果为LLM在瘤实践中的明智和基于证据的整合提供了关键指导.
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